Methods and system for analyzing multichannel electronic communication data

ABSTRACT

A method and system for analyzing electronic communication data is provided. In one embodiment, a method includes receiving electronic customer communication data by a contact center, analyzing the electronic customer communication data by applying a predetermined linguistic-based psychological behavioral model to the electronic customer communication data, and generating behavioral assessment data by the contact center based on said analyzing, the behavioral assessment data providing a personality type for the analyzed electronic customer communication data. In one or more embodiments, electronic customer communication data may be one or more of electronic-mail data, web content data, text message data, voice over IP data, online forum data, social media data, update status, media feed, social media review, social media data stream. In other embodiments, electronic customer communication data may include data received during a customer communication with a graphical user interface for the contact center.

TECHNICAL FIELD

The present disclosure relates generally to a method and system foranalyzing electronic communication data, and more particularly toapplying a psychological behavioral model to such electronic customercommunication data including, for example, one or more of electronicmail data, electronic social media data, and web content data, internetsurvey data.

BACKGROUND OF THE DISCLOSURE

It is known to use call centers to facilitate the receipt, response androuting of incoming telephonic communications relating to customerservice and sales. Generally, a customer communicates via telephone witha customer service representative (“CSR”) or contact center agent who isresponsible for responding to customer inquiries and/or directing thecustomer to an appropriate individual, department, information source,or service as required to satisfy the customer's needs.

It is also well known to monitor calls and other electroniccommunications between a customer and a call center. Accordingly, callcenters typically employ individuals responsible for listening to theconversation, or monitoring other types of electronic communications,between a customer and an agent. Many companies have in-house callcenters to respond to customer complaints and inquiries. In many cases,however, it has been found to be cost effective for a company to use athird party call center to handle such inquiries. Call centers may belocated thousands of miles away from a company's location or a customer.This often results in inconsistent and subjective methods of monitoring,training and evaluating contact center agents. These methods also mayvary widely from call center to call center.

For typical call centers, call monitoring may occur in real time. Insome instances, call centers may accumulate data for later review.Information gathered by a call center is typically used to provide acorrective response, to monitor agents of a call center and to identifypossible training needs. Based on the review and analysis of theincoming data, a monitor can make suggestions or recommendations toimprove the quality of the customer interaction.

Accordingly, there is a need in the field of customer relationshipmanagement (“CRM”) for an objective tool useful in improving the qualityof customer interactions with agents, and ultimately customerrelationships. In particular, a need exists for an objective monitoringand analysis tool which provides information about a customer'sperception of an interaction with a service. In the past,post-interaction data collection methods have been used to surveycallers for feedback. Although such surveys have enjoyed some degree ofsuccess, their usefulness is directly tied to a customer's willingnessto provide data after an interaction.

Recently, there has arisen an increase in the use of electronic mail,social media data feeds and web data and other electronic customercommunication data. Conventional call centers do not account for thecollection of this type of customer commentary regarding the quality ofproducts or services. As such, a need has arisen for an objective tooluseful for monitoring and analyzing not only telephonic communications,but also electronic data transmissions.

Certain psychological behavioral models have been developed as tools toevaluate and understand how and/or why one person or a group of peopleinteracts with another person or group of people. The ProcessCommunication Model® (“PCM”) developed by Dr. Taibi Kahler is an exampleof one such behavioral model. Specifically, PCM presupposes that allpeople fall primarily into one of six basic personality types: Reactor,Workaholic, Persister, Dreamer, Rebel and Promoter. Although each personis one of these six types, all people have parts of all six types withinthem arranged like a “six-tier configuration.” Each of the six typeslearns differently, is motivated differently, communicates differently,and has a different sequence of negative behaviors in which they engagewhen they are in distress. Importantly, each PCM personality typeresponds positively or negatively to communications that include tonesor messages commonly associated with another of the PCM personalitytypes. Thus, an understanding of a communicant's PCM personality typeoffers guidance as to an appropriate responsive tone or message. Thereexists a need for a system and method that analyzes the underlyingbehavioral characteristics of customer and agent communications byautomatically applying a psychological behavioral model such as, forexample PCM, to collected electronic data.

The embodiments described herein should overcome one or more of thedeficiencies of conventional systems and methods.

SUMMARY

According to one embodiment, a method is provided for analyzingelectronic customer communication data of one or more types. Electronicmail data, electronic social media data, and web content data (includinginternet survey data, blog data, microblog data, on line video data,discussion forum data and chat data), SMS data, VOIP data, and otherelectronic customer content data and voice data from telephoniccommunications. Once electronic customer communication data is received,customer identification data associated with the electronic customercommunication data is determined by the contact center the data, and thedata is analyzed by mining the data and applying a predeterminedlinguistic-based psychological behavioral model to the data. Accordingto one embodiment of the present method, behavioral assessment data isgenerated based on analyzing the electronic communication data. Thegenerated behavioral assessment data includes a personality typecorresponding to the analyzed electronic communication data.

According to another embodiment, a method of analyzing an electroniccustomer communication data includes aggregating electronic customercommunication data. In some embodiments, aggregated electronic customercommunication data may be used to generate a text file. The aggregatedelectronic communication data may be analyzed by mining the text fileand applying a predetermined linguistic-based psychological behavioralmodel to the text file. Behavioral assessment data is generated based onanalyzing the aggregated electronic communication data. The generatedbehavioral assessment data includes a personality type corresponding tothe analyzed aggregated electronic communication data.

According to another embodiment, a method of analyzing social media datais provided. Once social media data is received, the social media datacan be analyzed by mining the social media data and applying apredetermined linguistic-based psychological behavioral model to themined social media data. Behavioral assessment data is generated basedon the step of analyzing the electronic communication data. Thegenerated behavioral assessment data includes a personality typecorresponding to the analyzed electronic communication data.

According to another embodiment, a method of analyzing a telephoniccommunication between a first communicant to the telephoniccommunication and a second communicant to the telephonic communicationis provided. The method may include employing one or more types ofelectronic customer communication data with voice data to generatebehavioral assessment data. The telephonic communication is separatedinto at least first and second constituent voice data. The firstconstituent voice data is generated by the first communicant and thesecond constituent voice data is generated by the second communicant.The separated first and second constituent voice data is analyzed bymining the separated first and second constituent voice data andapplying a predetermined linguistic-based psychological behavioral modelto the separated first and second constituent voice data. Behavioralassessment data is generated that includes a personality typecorresponding to the analyzed constituent voice data based on the stepof analyzing one of the first and second constituent voice data.

According to one embodiment, either or both of the first and secondconstituent voice data is aggregated with the electronic communicationdata (i.e., one or more of the electronic mail data, electronic socialmedia data, and web content data), and the text file is generated fromthe aggregation. According to one embodiment, a text file is generatedthat is comprised of a textual translation of at least one of the firstand second constituent voice data before the analyzing step, theanalyzing step being performed on the text file.

In yet another embodiment, a method is provided for analyzing electroniccustomer communication data and generating behavioral assessment data.The method can include receiving electronic customer communication databy a contact center, determining customer identification data associatedwith the electronic customer communication data by the contact center,analyzing the electronic customer communication data by applying apredetermined linguistic-based psychological behavioral model to theelectronic customer communication data. The method may also includegenerating behavioral assessment data by the contact center based onsaid analyzing, the behavioral assessment data providing a personalitytype for the analyzed electronic customer communication data, andoutputting a notification including the behavioral assessment data bythe contact center based on detection of the customer identificationdata.

The methods described can be embodied in a non-transitory computerreadable medium adapted to control an executable computer readableprogram code for implementing one or more of the methods therein. Thecomputer program would include code segments or routines to enable allof the functional aspects of the interface described or shown herein.

According to still another embodiment, the computer program alsoincludes a code segment for generating a graphical user interface(“GUI”). The GUI can also be embodied in a computer program stored oncomputer readable media. The computer program would include codesegments or routines to enable all of the functional aspects of theinterface described or shown herein.

Other features and advantages will be apparent from the followingspecification taken in conjunction with the following drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is best understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not drawn to scale. In fact, the dimensions of the variousfeatures may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a simplified system diagram according to one or moreembodiments.

FIG. 2 is a simplified block diagram of a computing device according toone or more embodiments.

FIG. 3 is a simplified diagram according to one or more embodiments.

FIG. 4 is a schematic diagram illustrating a process of analyzingelectronic communication data in accordance with one or moreembodiments.

FIG. 5 is a method of analyzing electronic communication data accordingto one or more embodiments.

FIG. 6 is a flowchart depicting analysis of electronic customercommunication data according to one or more embodiments.

FIG. 7 is a flowchart depicting analysis of electronic customercommunication data according to one or more embodiments.

FIG. 8 is a graphical representation of providing a behavioral analyticsalert according to one or more embodiments.

FIG. 9 is a flowchart depicting output of data based on receivedelectronic customer communication data according to one or moreembodiments.

FIG. 10 is a flowchart depicting analysis of electronic customercommunication data according to one or more embodiments.

FIGS. 11A-11B are schematic diagrams of a telephonic communicationsystem according to one or more embodiments.

FIG. 11C is a schematic diagram of a telephonic communication systemwith a multi-port PSTN module according to one or more embodiments.

FIG. 12 is a flow chart illustrating a process of recording andseparating a telephonic communication according to one or moreembodiments.

FIG. 13 is a flow chart illustrating a process of recording andseparating a telephonic communication according to one or moreembodiments.

FIG. 14 is a flow chart illustrating a process of analyzing separatedconstituent voice data of a telephonic communication in according to oneor more embodiments.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It is nevertheless understood that no limitation tothe scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, and methods, and anyfurther application of the principles of the present disclosure arefully contemplated and included within the present disclosure as wouldnormally occur to one of ordinary skill in the art to which thedisclosure relates. In particular, the features, components, and/orsteps described with respect to one embodiment may be combined with thefeatures, components, and/or steps described with respect to otherembodiments of the present disclosure. For the sake of brevity, however,the numerous iterations of these combinations will not be describedseparately.

FIG. 1 is a simplified system diagram of a system including contactcenter 105 according to one or more embodiments. As shown in FIG. 1,contact center 105 may include one or more servers, such as servers 106_(1-n) for receiving electronic customer communication data.

A “contact center” as used herein can include any facility or systemserver suitable for receiving and recording electronic communicationsdata from customers. In one embodiment, electronic customercommunication data can include, for example, telephone calls, facsimiletransmissions, web interactions, voice over IP (“VoIP”), and video datawith contact center 105. According to another embodiment, electroniccustomer communication data is at least one of electronic-mail data, webcontent data, text message data, voice over IP data, and online forumdata received by contact center 105. In certain embodiments, electroniccustomer communication data may include social media data, such as oneor more of an update status, media feed, social media review, and asocial media data stream. According to another embodiment, electroniccustomer communication data may be provided by a customer duringcommunication with contact center 105, the electronic customercommunication data being associated with one or more of a pop-up windowmessage, computer display, and application window and graphical userinterface for the contact center. Contact center 105 may be configuredto aggregate electronic customer communication data from one or morechannels and generate behavioral assessment data based on the aggregateddata.

Contact center 105 may be configured to receive electronic customercommunication data from customers via one or more channels, or mediums,in order to allow for multi-channel input of customer data andinteractions. As shown in FIG. 1, contact center 105 may interface withone or more of customers 115 _(1-n) via communication network 110.According to another embodiment, contact center 105 may receiveelectronic customer communication data from one or more devices, such asmobile communication device 120, fax machine 125, computer 130 andserver 135. Communications with contact center 105 via communicationnetwork 110 may be transmitted by and through any type oftelecommunication device and over any medium suitable for carrying data.For example, the communications may be transmitted by or throughtelephone lines, network cable or wireless communications.

In certain embodiments, electronic customer communication data may bereceived by one or more of servers 106 _(1-n) of contact center 105based on a user interface provided contact center 105. By way ofexample, contact center 105 may provide a user interface, such as a website or web portal that may be accessed for providing one or more oftelephonic communications relating to one or more of customer service,customer satisfaction, customer preference, customer retention, andsales. In some embodiments, servers 106 _(1-n) of contact center 105 mayeach be configured to capture specific channels. In other embodiments,contact center 105 may communicate with a server, such as server 135,configured to provide a user interface of customer interaction. One ormore of servers 106 _(1-n) and server 135 may be configured to provide avirtual portal or user interface for contact center 105. In certainembodiments, server 135 may be a third-party server configured toreceive electronic customer communication data, and to transmit thereceived electronic customer communication data to contact center 105.By way of example, server 135 may be a third-party server hosting anetwork service (e.g., web based service, social media service, etc.)configured to collect and store customer data. In some instances, server135 may be associated with a social media service. As will be discussedin more detail below, contact center 105 may be configured to monitorand/or analyze electronic customer communication data received formserver 135. In one embodiment, analysis of the electronic customercommunication data received from server 135 may be used by contactserver 105 to evaluate and monitor server 135.

Contact center 105 may be configured to receive and record varyingelectronic communications and data formats that represent an interactionwith one or more customers. Each type of electronic customercommunication may relate to a channel. Accordingly, by receiving one ormore types of electronic customer communication data behavioralassessment data may be generated for multichannel applications. Incertain embodiments, the method for analyzing an electroniccommunication between a customer and a contact center can be implementedby a computer program. In more specific terms, computer hardwareassociated contact center 105 may be operating on or more computerprograms that may be used in connection with embodiments describedherein.

Contact center 105 may be configured to provide customer serviceresources for one or more entities (e.g., company, corporation,government entity, utility, voice and/or data service providers,broadcasters, etc.). In certain embodiments, contact center 105 may beassociated with a particular entity. In other embodiments, contactcenter 105 may be provided by a third-party for providing customerservice to a plurality of entities.

As shown in FIG. 1, contact center 105 may interface with one or morecustomers 115 _(1-n). As used herein, a “customer” may be a purchaser orregistered user of a service, an entity acting on behalf of a purchaseror registered user of service, an unregistered user of service, and/orentity communicating with contact center 105. Contact center 105 may beconfigured to track and/or store contact information, identifyinginformation, and electronic customer communication data for one or morecustomers.

FIG. 2 is a simplified block diagram of a computing device according toone or more embodiments. Computing device 200 may be configured toreceive electronic customer communication data and generate behavioralassessment data according to one or more embodiments. For purposes ofunderstanding the hardware as described herein, the terms “computer” and“server” have identical meanings and are interchangeably used. Computingdevice 200 includes processor 205, and memory 210 storing a computerprogram product 215. As shown in FIG. 2, computing device 200additionally includes local interface 220 and communication interface230. Processor 205 can be a hardware device for executing software,including computer program 215 stored in memory 210. Processor 205 canbe a custom made or commercially available processor, a centralprocessing unit (CPU), an auxiliary processor among several processorsassociated with the computing device 200, a semiconductor basedmicroprocessor (in the form of a microchip or chip set), amacroprocessor, or generally any device for executing softwareinstructions.

According to one or more embodiments, memory 210 can include any one, orcombination of, volatile memory elements (e.g., random access memory(RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements(e.g., ROM, hard drive, tape, CDROM, etc.). Moreover, memory 210 mayincorporate electronic, magnetic, optical, and/or other types of storagemedia. In some embodiments, memory 210 may have a distributedarchitecture where various components are situated separate from, orremote from, one another, but can be accessed by the processor 205.Memory 210 can include one or more separate programs, each of whichhaving an ordered listing of executable instructions for implementinglogical functions. For example, memory 210 can include computer program215 and one or more programs for providing an operating system (O/S).Computer program product 215 can be implemented in software (e.g.,firmware), hardware, or a combination thereof. Computer program 215 maybe a control system, a source program, executable program (object code),script, or any other non-transitory computer readable code comprisinginstructions to be performed. When computing device 200 is in operation,the processor 205 is configured to execute software stored within thememory 210, to communicate data to and from the memory 210, and togenerally control operations of the computing device 200 pursuant to thesoftware.

In one embodiment, computer program 215 may be implemented in softwareand may be stored on any non-transitory computer readable medium for useby or in connection with any computer related system or method. In thecontext of this document, a “computer-readable medium” can be anynon-transitory means that can store, communicate, propagate, ortransport the program for use by or in connection with the instructionexecution system, apparatus, or device. The computer readable medium canbe, for example but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, ordevice. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: an electricalconnection (electronic) having one or more wires, a portable computerdiskette (magnetic), a random access memory (RAM) (electronic), aread-only memory (ROM) (electronic), an erasable programmable read-onlymemory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber(optical), and a portable compact disc read-only memory (CDROM)(optical). In another embodiment, computer program 215 may beimplemented in hardware by one or more of a discrete logic circuit(s)having logic gates for implementing logic functions upon data signals,an application specific integrated circuit (ASIC) having appropriatecombinational logic gates, a programmable gate array(s) (PGA), a fieldprogrammable gate array (FPGA), etc.

I/O devices 225 may include input devices, for example but not limitedto, a keyboard, mouse, scanner, microphone, touch screens, interfacesfor various medical devices, bar code readers, stylus, laser readers,radio-frequency device readers, etc. Furthermore, the I/O devices 225may also include output devices, for example but not limited to, aprinter, bar code printers, displays, etc. I/O devices 225 may furtherinclude devices that communicate both inputs and outputs, for instancebut not limited to, a modulator/demodulator (modem; for accessinganother device, system, or network), a radio frequency (RF) or othertransceiver, a telephonic interface, a bridge, a router, etc. In certainembodiments processor 205 of computing device 200 may includecommunication interface 230 for communication with one or more or otherdevices via wired and/or wireless communication. Communication interface230 may be configured to allow for communication with via acommunication network (e.g., communication network 110 of FIG. 1).

Referring now to FIG. 3, a simplified system diagram is shown accordingto one or more embodiments. According to one embodiment, system 300includes server 305 configured to receive electronic customercommunication data. Server 305 may be associated with a contact center(e.g., contact center 105 of FIG. 1) and may be configured to receivecommunications of one or more types (e.g., multichannel) from customersand/or electronic data sources via communication network 315. As shownin FIG. 3, system 300 includes server 305, associated with a contactcenter, however, it should be appreciated that system 300 may include aplurality of servers associated with a contact center. In certainembodiments, server 305 may be located within a contact center. In otherembodiments, server 305 may be a shared server (e.g., cloud sever, etc.)configured to provide data to a contact center. Server 305 may beconfigured to receive and store electronic customer data in data storage310, which may include one or more memory storage devices. In certainembodiments, data storage 310 may be distributed storage. According toone embodiment, server 305 may analyze electronic customer communicationdata received from one or more channels. As such, multi-channelelectronic customer communication data may be received, stored, and/oranalyzed for one or more customers.

Server 305 may be configured to communicate with one or more devices,such as mobile device 320, computing device 325, servers 330 and/orpersonal computing device 340. As will be discussed below, electroniccommunication data, from these devices may be received by server 305 andanalyzed based on the type of communication employed. By allowing formultiple channels of communication with server 305, a plurality of datatypes may be used to generate behavioral assessment data for a user.

According to one embodiment, devices for communicating with server 305,such as device 325, may be smart computing devices. Smart computingdevices can be computing devices such as laptop or desktop computers,smartphones, PDAs, portable media players, tablet computers, televisionsor other displays with one or more processors coupled thereto orembedded therein, or other appropriate computing devices that can beused to for displaying a web page or web application.

In one embodiment, server 305 can include a network address forreceiving electronic customer communication data over communicationnetwork 315. By way of example, a contact center may have one or morecontact numbers (e.g., telephone, text messaging, short message service(SMS), multimedia message service (MMS), etc.) or network addresses(e.g., email, electronic messaging, voice over IP, IP address, etc.).Customers may communicate with contact center 305 by sending one or moremessages to the contact center. In some cases, electronic communicationdata may be initiated by a customer regarding a request for service, achange to service, information for an account, and/or to speak with anagent associated with a service.

According to another embodiment, a contact center may receive electroniccustomer data from one or more third-party servers. Servers 330 may bethird-party servers, such as servers responsible for hosting a socialnetworking site, including calendar functions, message boards,synchronous communications, forum discussions and the like. Servers 3330can also be responsible for sending and receiving electronic messages todevices 320, 325 and 340 via communication network 315 (for example, aLAN, WAN, WiFi, or the Internet) or causing such messages to be sent.Servers 330 also may be configured to provide one or more applicationprogramming interfaces (APIs) for the integration of mobile-to-webextension applications.

Servers 330 may be configured to provide a user interface (e.g.,website, portal, etc.) for customers to access data for accounts and/orservice. In one embodiment, servers 330 may be social media serversincluding accounts for the social media service of customers and one ormore accounts for a contact center. Servers may allow for customers tointeract with the profile or account setup by the contact center via oneor more applications or programs run by servers 330. Server 305 may beconfigured to receive data for one or more accounts of the contactcenter on servers 330 and via one or more application programminginterfaces (APIs). In certain embodiments, server 305 may be configuredto fetch data. In other embodiments, server 305 may be configured toreceive pushed data.

According to another embodiment, one or more of devices 320, 325 and 340may include applications (e.g., apps) that are dedicated for providing auser interface on the device for communicating and receiving data fromserver 305. In addition to providing electronic customer communicationdata, the applications may provide identification of a customer or userof a device, usage details, metadata, application interaction data, etc.

According to one or more embodiments, behavioral assessment data may begenerated by analyzing electronic customer communication data. Analyzingelectronic customer communication data and generating behavioralassessment data are discussed below with reference to FIGS. 4-10.According to one or more embodiments, electronic customer communicationdata can be one or more of electronic mail data, electronic social mediadata, and web content data electronic mail data, electronic social mediadata, and web content data (including internet survey data, blog data,micro-blog data, on line video data, discussion forum data, chat feeddata), SMS data, VoIP data, and other electronic customer content dataand voice data generated during a telephonic communication.

In FIG. 4, a schematic diagram illustrates a process of analyzingelectronic communication data in accordance with one or moreembodiments. In one embodiment, electronic customer communication datacan be received at block 405. At block 410, an analytics engine cananalyze the electronic customer communication data received at block405, which can include one or more of mining data from thecommunication, converting data in the communication to a file,determining the sender of the data, identifying a customer associatedwith the data, determining the reason for the data being sent,identifying the type of communication that is being sent, aggregatingdata, etc. In certain embodiments, analyzing at block 410 may includeapplying one or more behavioral models to electronic customercommunication data. A behavioral model may apply one or more analyticengines for detecting behavioral signifiers, detecting customerinterests and personality types.

In block 415, electronic customer communication data may be stored basedon one or more attributes or fields of interest. As shown in FIG. 4,electronic customer communication data may be profiled for storage atblock 415 based on one or more of customer index, personality, attitude,megaphone, and a web content score. For example, data may becharacterized by one or more profile fields. It should be appreciatedhowever, that other types of profile information may be employed atblock 415. Based on profile information determined at block 415, datamay be generated or added to provide a data mart for customers. By wayof example, data determined for a particular customer may be storedand/or associated with a particular customer profile at block 420.Customer profiles may be assigned one or more identification numbers,including but not limited to an account number, mobile communicationnumber, etc. In certain embodiments, customer data stored at block 420may be based on one or more network communication addresses employed forcontacting a contact center.

According to another embodiment, data profiled at block 415 may beprovided to one or more of a profile engine at block 425, influenceengine at block 430, and reporting engine at block 435 for generatingand outputting behavioral assessment data. The profile engine at block425 may be configured to match electronic customer communication data toone or more profiles. The influence engine at block 430 may beconfigured to assess the relevancy of electronic customer communicationdata. For example, influence engine 430 may select or discard recent orpreviously stored electronic customer communication data based on therelevancy to a particular series of events or an incoming communicationfrom a customer. The reporting engine at block 435 may be configured togenerate reports of electronic customer communication data.

Referring now to FIG. 5, a method of analyzing electronic communicationdata is shown according to one or more embodiments. Process 500 of FIG.5 may be employed by a contact center, and/or performed by one or moreservers associated with a contact center. Process 500 may be initiatedby receiving electronic customer communication data at block 505. In oneembodiment, electronic customer communication data at block 505 caninclude one or more of a telephone call, facsimile transmission, webinteraction, voice over IP (“VoIP”) data, and video data received by oneor more servers associated with a contact center. According to anotherembodiment, electronic customer communication data is at least one ofelectronic-mail data, web content data, text message data, voice over IPdata, and online forum data received by one or more servers associatedwith a contact center. In certain embodiments, electronic customercommunication data at block 505 may include social media data, such asone or more of an update status, media feed, social media review, and asocial media data stream. Electronic customer communication datareceived at block 505 may be from one or more channels. As will bediscussed below, the electronic customer communication data from one ormore channels may be multi-channel data (e.g., received from one or morechannels and/or source types), which can be aggregated to generatebehavioral and/or analytic data by one or more servers of a contactcenter.

According to another embodiment, electronic customer communication datamay be provided by a customer during communication with contact center,the electronic customer communication data being associated with one ormore of a pop-up window message, computer display, and applicationwindow and graphical user interface for the contact center. By way ofexample, electronic customer communication data can be generated by acomputer or mobile device operated by a user to communicate with thecontact center. In certain cases, an application on the customersdevice, such as an application for monitoring an account and payingbills for a service associated with the contact center, can transmitelectronic customer communication to a server associated with a contactcenter.

At block 510, process 500 analyzes the electronic customer communicationdata received at block 505. Analysis of electronic customercommunication data may be performed by one or more servers of a contactcenter. In one embodiment, electronic customer communication data isanalyzed at block 510 by mining the data and applying a predeterminedlinguistic-based psychological behavioral model to the electroniccommunication data. For examiner, the electronic customer communicationdata may be mined to determine the purpose of the communication,customer activity associated with the contact center, a type oftransaction, behavioral signifiers, etc. Analysis at block 510 mayinclude identifying or determining the type of communication in whichthe electronic customer communication data is associated with. As willbe discussed in more detail below with reference to FIG. 6, electroniccustomer communication data identified with a customer may be storedand/or aggregated. Process 500 may include one or more servers of acontact center determining customer identification data associated withthe electronic customer communication data. Analysis at block 510 of theelectronic customer communication data is associated may be based on thetype of data detected. For example, for certain types of communication,a behavior model may assess the criteria based on one or more signifiersparticular to the communication type.

Analysis at block 510 may include determining identifying indicia of acustomer and/or a behavioral signifier in the electronic customer data.Table 2 provides behavioral signifiers (i.e., words) that may beassociated with a corresponding behavioral type in the PCM Modelaccording to one or more embodiments.

Behavioral assessment data is generated at block 515. In one embodiment,process 500 generates behavioral analysis data based on the analysis ofreceived electronic customer communication data at block 515 and datamined from the communication. As will be discussed below, behavioralassessment data, generated by one or more servers of the contact center,includes a personality type corresponding to the analyzed electroniccommunication data. Behavioral assessment data generated by process 500may be employed by the contact center in one or more ways. In oneembodiment, behavioral assessment data may be employed by the contactcenter to assist customers. As will be discussed in more detail belowwith respect to FIG. 9, one or more notifications of behavioral data maybe output to an agent of a contact center to provide the context of acustomer's interaction with a contact center and intent for contacting acontact center. According to another embodiment, behavioral assessmentgenerated by process 500 may be employed for one or more analytics tomonitor a customer agent of a contact center, third party serviceprovided of the contact center and the customer service of the contactcenter as a whole. The behavioral assessment data can allow formonitoring effectiveness of a contact center agent, third party callcenter, etc., by providing performance metrics rating the effectivenessbased on a behavioral model and/or one or more performance managementcriteria. In addition to monitoring effectiveness, behavioral assessmentdata can provide output data characterizing one or more of customersatisfaction, ability to correct customer needs, and improvements tocustomer service.

In another embodiment, multiple forms of electronic communication dataare received at block 505 and aggregated at block 510. A text file mayoptionally be generated from aggregated electronic customercommunication data at block 525. The aggregated electronic communicationdata can be analyzed by mining the text file and applying apredetermined linguistic-based psychological behavioral model to thetext file at block 525. Behavioral assessment data may then generatedbased on the aggregated electronic communication data.

According to another embodiment, a psychological behavioral model usedto analyze the electronic customer communication data is the ProcessCommunication Model® (“PCM”) developed by Dr. Taibi Kahler. PCM is apsychological behavioral analytic tool which presupposes that all peoplefall primarily into one of six basic personality types: Reactor,Workaholic, Persister, Dreamer, Rebel and Promoter. Although each personis one of these six types, all people have parts of all six types withinthem arranged like a six-tier configuration. Each of the six typeslearns differently, is motivated differently, communicates differently,and has a different sequence of negative behaviors they engage in whenthey are in distress. Importantly, according to PCM, each personalitytype of PCM responds positively or negatively to communications thatinclude tones or messages commonly associated with another of the PCMpersonality types. Thus, an understanding of a communicant's PCMpersonality type offers guidance as to an appropriate responsive tone ormessage or wording.

According to the PCM Model, behavioral characteristics are associatedwith the respective personality types. For example, Table 1 providesbehavioral characteristics that may be associated with a correspondingbehavioral type in the behavioral model employed by process 500.

TABLE 1 Process Communication Model (PCM) Personality Type BehavioralCharacteristics Reactors compassionate, sensitive, and warm; great“people skills” and enjoy working with groups of people Workaholicsresponsible, logical, and organized Persisters conscientious, dedicated,and observant; tend to follow the rules and expect others to follow themDreamers reflective, imaginative, and calm Rebels creative, spontaneous,and playful Promoters resourceful, adaptable, and charming

The behavioral characteristics of Table 1 may be categorized by words,tones, gestures, postures and facial expressions, and can be observedobjectively with significantly high interjudge reliability.

Behavioral assessment data may be based on comparison of the electroniccustomer communication data to one or more libraries of data, whereinone or more words or phrases can be evaluated. For example, behavioraldata generated at block 515 may assess a level of distress, whereinelectronic customer communication data including the term “manager” willescalate the communication. In that fashion, a language based model maybe applied to customer communications. The model may apply linguisticanalysis to received data to create one or more structures evaluatingthe customer, communication and contact center.

Behavioral assessment data may be stored at block 520 by one or moreservers associated with a contact center. In one embodiment, behavioraldata may be stored based on one or more customer identifiers determinedat block 510. According to another embodiment, behavioral assessmentdata may be stored at block 520 by associated the data with identifyingindicia associated with a customer, such as one or more fields,profiles, identification numbers, account numbers, email addresses,source identifier of the communication type, IP addresses, and uniqueidentifier associated with app. As will be discussed in more detailbelow with respect to FIG. 9, behavioral assessment data generated basedon electronic customer communication data, such as behavioral assessmentdata by process 500, may be output. For example, a notification may beoutput including the behavioral assessment data by the contact centerbased on detection of the customer identification data.

Behavioral data may be generated in real-time, or near real-time. Inother embodiments, analysis and/or generation of behavioral data may bepost processed.

According to one embodiment, behavioral assessment data stored at block520 may be used to for assessment of customers and a contact center. Incertain embodiments, for example, the behavioral assessment data may beused to rate ability of customer service rep, interactive voice response(IVR), etc.

According to one embodiment, one or more methods are provided foranalyzing social media data. Accordingly, process 500 of FIG. 5 mayanalyze social media data to generate behavioral assessment data. By wayof example, social media data may be received at block 505 and analyzedat block 510 by mining the social media data and applying apredetermined linguistic-based psychological behavioral model to themined social media data. Behavioral assessment data may be generated atblock 515 based on the analyzing of block 510. The behavioral assessmentdata generated at block 515 can include a personality type correspondingto the analyzed electronic communication data. It will be understoodthat electronic communications received in accordance with any of themethods described may be either actively retrieved from a source ortransmitted by a source. Social media data may also include one or moreof captured image/video data, and employee data.

FIG. 6 is a flowchart depicting analysis of electronic customercommunication data according to one or more embodiments. Process 600 maybe employed by a contact center, and/or performed by one or more serversassociated with a contact center. Process 600 of FIG. 6 may be initiatedby receiving electronic customer communication data at block 605. Atblock 610, process 600 includes analyzing the electronic customercommunication to identify a customer based on the data received at block605. Identification of the customer at block 610 may include determiningone or more of a customer identification number, such as a numberidentifying customers of the contact center and one or more identifiersassociated with the electronic customer communication data type.Identifiers of the electronic customer communication data type mayinclude a network address (e.g., IP address, email address, networklogin, etc.), device number (telephone/mobile device number), andidentification numbers in general. In certain embodiments, when acustomer transmits electronic customer communication data via a networkbased application, such as application or via a web address associatedwith the contact center the customer identification may be determinedfrom the contacts of the communication and/or the metadata associatedwith the transmission. When electronic customer communication data isassociated with a social media source, a customer for the electroniccustomer communication data may be identified based on a social mediaidentification, or handle for employed for the communication. Customeridentification may be determined based on one or more of customer lists,assuming customer identification, matching received data to a customeridentification table, user login data, and metadata associated withelectronic customer communication data. In certain embodiments,identification of a customer at block 610 may be auto generated based onthe type of communication, content of the communication and source. Inthat fashion a uniform type of identifying customers may be applied toaid in matching data.

At decision block 615, process 600 determines if customer identificationdetermined at block 610 matches one or more customer identifiers. Incertain embodiments, a contact center may store electronic customercommunication data based on a customer identifier. One or more customeridentifiers may be associated with the same customer. For example, aparticular customer may be identified based on one or more of atelephone number, account number, email address, service code, etc. Whenthe contact center determines an identifier match at block 615 (e.g.,“YES” path out of decision block 615), the electronic customercommunication data can be stored at block 620. Storing data at block 620may include storing data for only one customer, or in some cases storingthe electronic customer communication data for more than one customer.When the contact center does not determine an identifier match at block615 (e.g., “NO” path out of decision block 615), process 600 may includedetermining if the electronic customer communication data matches aconversation at block 625. Storing data at block 620 may include storingdata for only one customer or, in some cases, storing the electroniccustomer communication data for more than one customer.

According to another embodiment, process 600 may include storing and/oraggregating data at block 620. Aggregating the data at block 620 mayinclude combining at least a portion of the received electronic customercommunication data with other received electronic customer communicationdata. For example, text provided by a customer in a text communication(e.g., text message, email, etc.) may be aggregated with voice datareceived associated with a separate electronic customer communicationdata. In that fashion, analysis using a behavioral model or generationof behavioral assessment data may be based on aggregated electroniccustomer communication data. Aggregating electronic customercommunication data at block 620 may be from one or more sources based onidentification of a customer from the electronic customer communicationdata. Aggregating at block 620 may include aggregating voice data withthe electronic customer communication data. By way of example,electronic customer communication data, such as an email in someembodiments, may be analyzed with voice data associated with one or moreconstituents to generate behavioral data for a customer.

A conversation match at block 625 may be determined by comparing one ormore of the content of electronic customer communication data andmetadata of the electronic customer communication data to previouslyreceived electronic customer communication data. For example, electroniccustomer communication data may relate to a web based chat (e.g., textconversation via pop-up window on a website) associated with the contactcenter, wherein text of the conversation (e.g., electronic customercommunication data) can be matched with text during a previousconversation based on the details of the conversation, network addressesor even customer service agent associated with the electronic customercommunication data. When the contact center determines a conversationmatch at block 625 (e.g., “YES” path out of decision block 625), theelectronic customer communication data can be stored at block 630 for apredetermined time. Storage of the customer communication data for apredetermined time may allow for matching the data to other electroniccustomer communication data to determine a customer identifier. When thecontact center does not determine a conversation match at block 625(e.g., “NO” path out of decision block 625), the electronic customercommunication can be discarded at block 635.

Referring now to FIG. 7, a flowchart is shown depicting analysis ofelectronic customer communication data according to one or moreembodiments. Process 700 may be employed by a contact center, and/orperformed by one or more servers associated with a contact center.According to one embodiment, process 700 includes applying an analytictool to received and/or stored electronic customer communication data.Process 700 of FIG. 7 may be initiated by translating electroniccustomer communication data to text at block 705. The translatedelectronic data may be mined at block 710 for behavioral signifiers.According to one embodiment, the electronic customer communication datais mined for significant words at block 710.

Mined electronic customer communication data may be compared to a systemdatabase at decision block 715 to determine a behavior match. Accordingto one embodiment, determining a behavior match at decision block 715includes applying a behavior model, such as PCM, to the identifiedwords. For example, Table 2 provides behavioral signifiers (i.e., words)that may be associated with a corresponding behavioral type in the PCMModel according to one or more embodiments.

TABLE 2 PROCESS COMMUNICATION MODEL (PCM) PERSONALITY TYPE BEHAVIORALSIGNIFIERS Reactors Emotional Words Workaholics Thought Words PersistersOpinion Words Dreamers Reflection Words Rebels Reaction Words PromotersAction Words

Determining a behavioral match at decision block 715 can includeexecuting the identified behavioral signifier against a system databasewhich maintains all of the data related to the psychological behavioralmodel. When a behavioral signifier does not match a behavior type (e.g.,“NO” path out of decision block 715), the behavioral signifier andelectronic customer communication data may be stored at block 720. Whena behavioral signifier includes a behavior match (e.g., “YES” path outof decision block 715), the behavior type may be determined at block725. According to one embodiment, the behavior type may be determined atblock 725 based on the behavioral signifiers identified in theelectronic communication data, and predetermined algorithm to decipher alinguistic pattern that corresponds to one or more of the PCMpersonality types. More specifically, the present method mines forlinguistic indicators (words and phrases) that reveal the underlyingpersonality characteristics of the electronic communicant during periodsof distress. According to one embodiment, determining a behavior type atblock 725 may be based on six personality types, generally referred toas 730 in FIG. 7. It should be appreciated, however, that determining abehavior type at block 725 may be based on additional, or fewer,personality types.

The resultant behavioral assessment data is stored at block 735 (e.g.,in a database) so that it may subsequently be used to comparativelyanalyze against behavioral assessment data derived from analysis ofresponsive communications. According to one embodiment, generatingbehavioral assessment data at block 735 includes consideration of wordsegment patterns of all communicants as a whole to refine the behavioralassessment data of each electronic communicant, making sure that thefinal behavioral results are consistent with patterns that occur inhuman interaction. Alternatively, raw behavioral assessment datagenerated at block 735 may be used to evaluate qualities of a singlecommunicant (e.g., the customer or agent behavioral type, etc.). Theresults generated by analyzing electronic customer communication datathrough application of a psychological behavioral model can begraphically illustrated through a GUI.

According to another embodiment, process 700 may be repeated whenelectronic customer communication data is received. For example, whenadditional electronic customer communication data is received,behavioral assessment data maybe re-calculated or generated based on theadditional data. Alternatively, when a different type of electroniccustomer communication data is received, such as electronic customercommunication data from another channel than originally received, theelectronic customer communication data may be aggregated to prior tomining translation data or generating behavioral assessment data.

It should be noted that, although one preferred embodiment uses PCM as alinguistic-based psychological behavioral model, any knownlinguistic-based psychological behavioral model be employed. Inaddition, more than one linguistic-based psychological behavioral modelcan be used to analyze an electronic communication.

According to one embodiment, the generated behavioral assessment data atblock 735 is associated with at least one identifying indicia andstored. For example, the identifying indicia can be a name, e-mailaddress, account number, or other indicia sufficient to identify thesource of the behavioral assessment data. In this way, the behavioraldata corresponding to at least one identifying indicia and being madeavailable for subsequent analysis.

According to another embodiment, responsive electronic communicationdata to the electronic communication data can be automaticallygenerated. For example, the responsive electronic communication can takethe form of any responsive communication such as, for example, aresponsive e-mail, electronic post, social media feed or telephonicresponse.

FIG. 8 is a graphical representation of providing a behavioral analyticsalert according to one or more embodiments. According to one or moreembodiments, it may be advantageous to provide an alert associated withcustomer interactions. For example, a contact center may be configuredto provide an alert to an agent of a contact center, or CSR, during acommunication session between the agent and customer. According to oneembodiment, an alert may be generated and provided by a contact center,and/or performed by one or more servers associated with a contactcenter. Generating alerts may include using one or more of the methodsand acts described herein with respect to receiving and analyzingelectronic customer communication data.

An interaction of a customer 805 with server 810 and a device 815forming multiple interactions with a contact center is depicted in FIG.8. It should be appreciated that principles of the interactions depictedin FIG. 8 may be applied to one or more other embodiments describedherein. As shown in FIG. 8, customer 805 may interact with device 815,which may mobile device, smart phone, tablet, etc., to interact with acontact center. By way of example, customer 805 may initiate action 816to make a payment for a service to a contact center using device 815which can initiate communication between device 815 and server 810 asshown by 817. The actions by the user may be considered an interaction.It should also be appreciated that other actions by a user with acontact center may relate to interactions. In certain embodiments, whenthe interaction with device 815 is associated with a web application oran application stored on device 815, data shown as 818 may be providedto an analytics engine 820 of the contact center. The analytics enginemay analyze any electronic customer communication data provided to thecontact center, such as by using the process described in FIGS. 5-7above. Based on the interaction, analytics engine 820 can providebehavioral data to server 810. According to another embodiment, server810 may automatically generate responsive electronic communications toelectronic customer communication data received from customer 805.Automatically generated responsive electronic communications may includea displayed message on the device the user employed to transmitelectronic customer communication data, and/or may be a return messagebased on the same type as received electronic customer communicationdata.

According to another embodiment, behavioral data may be stored and/oraccessed by server 810 when customer 805 initiates contact with thecontact server, as shown by 821. By way of example, customer 805 mayinitiate a communication 821 with a contact center when a payment viadevice 815 has failed. Based on communication 821, the contact centermay connect customer 805 to an agent or CSR, such as agent 825. In oneembodiment, the agent may be provided behavioral data, shown as 830, byserver 810. Providing behavioral data for a customer to an agent of thecontact center can provide context of a customer's interaction andintent with the contact center.

An exemplary alert is shown as 840, according to one or moreembodiments. Alert 840 may provide information associated with thecustomer and customer's interactions with a contact center. As shown inFIG. 8, alert 840 includes customer identification 845 (e.g., callername), an indication 850 whether self-service occurred in the last 24hours, an indication 855 of the last time self-service was provided, andan instructions 860 for the agent. Indications shown in FIG. 8 for alert840 are exemplary, and it should be appreciated that an alert may beconfigured to provide additional and/or different information.

Referring now to FIG. 9, a flowchart is depicted of outputting databased on received electronic customer communication data according toone or more embodiments. According to one or more embodiments, it may beadvantageous to output behavioral data generated for a customer and/oragent of a contact center. For example, behavioral data for a customermay be output to a graphical user interface or display screen for use byan agent (e.g., CSR) during a telephone call to a contact center.According to another embodiment, behavioral data (e.g., alert 840) for acustomer may be output to a graphical user interface or display screenfor assessing and/or monitoring performance of a CSR. Process 900 may beemployed by a contact center, and/or performed by one or more serversassociated with a contact center. Process 900 may employ one or more ofthe methods and acts described herein with respect to receiving andanalyzing electronic customer communication data.

Process 900 may be initiated by receiving electronic customercommunication data at block 905. In certain embodiments, the electroniccustomer communication data may be received during a voice communicationbetween a first constituent that is a customer, and a second constituentthat is an agent of the contact center. Based on the received electroniccustomer communication data, a customer may be identified at block 910.

At block 910, for example, voice data associated with an audio waveformof the voice communication can be mined and analyzed using multi-stagelinguistic and non-linguistic analytic tools. The analysis data can bestored and can be accessed by a user (e.g., CSR supervisor) through aninterface portal for subsequent review. The digital stereo audiowaveform is compressed and stored in an audio file which is held on aserver for subsequent access through the interface portal. In oneembodiment, audio compression is postponed until analysis of the audiodata is complete. The delay allows the system to apply the analytictools to a truer and clearer hi-fidelity signal. The system employedalso minimizes audio distortion, increases fidelity, eliminates gaincontrol and requires no additional filtering of the signal.

According to one embodiment, process 900 may search for behavioral datafor the customer based at block 915 based on the identification at block910. When behavioral data is detected at block 915, a graphical userinterface may be configured to display or output the behavioral data. Inone embodiment, the output of behavioral data, for example, may aide anagent of the contact center, such as a CSR, to assist the customer. Inother embodiments, the behavior data may relate to the agent of thecontact center and may be displayed to a manager of the agent at the CSRfor evaluating performance and monitoring. In other embodiments, thedata output may be associated with the contact centers agent in order toprovide the agent with feedback of their performance.

FIG. 10 is a flowchart depicting analysis of electronic customercommunication data according to one or more embodiments. Process 1000may be employed by a contact center, and/or performed by one or moreservers associated with a contact center. According to one embodiment,process 1000 includes generating behavioral assessment data to receivedand/or stored electronic customer communication data. In particular,FIG. 10 illustrates a general flow for analyzing voice data generatedduring a telephonic communication including separating a telephoniccommunication into first constituent voice data and second constituentvoice data.

Process 1000 of FIG. 10 may be initiated by separating electroniccustomer communication data to text at block 1005. For example, anuncompressed digital stereo audio waveform of a conversation between acustomer and a contact center agent is recorded and separated intocustomer voice data and contact center agent voice data at block 1005.

At block 1010, one of the first or second constituent voice data is thenseparately analyzed by applying a predetermined psychological behavioralmodel thereto to generate behavioral assessment data. In one embodimentdiscussed below, linguistic-based behavioral models are adapted toassess behavior based on behavioral signifiers within a communicationsare employed. One or more psychological behavioral models may be appliedto the voice data to generate behavioral assessment data at block 1015.

According to one embodiment, telephonic communications analyzed byprocess 1000 can be one of numerous calls stored within a contact centerserver, or communicated to a contact center during a given time period.Accordingly, the telephonic communication being subjected to analysis isselected from the plurality of telephonic communications. The selectioncriteria for determining which communication should be analyzed mayvary. For example, the communications coming into a contact center canbe automatically categorized into a plurality of call types using anappropriate algorithm, such as a word-spotting algorithm thatcategorizes communications into particular types or categories based onwords used in the communication. In one embodiment, each communicationis automatically categorized as a service call type (e.g., a callerrequesting assistance for servicing a previously purchased product), aretention call type (e.g., a caller expressing indignation, or having asignificant life change event), or a sales call type (e.g., a callerpurchasing an item offered by a seller). In one scenario, it may bedesirable to analyze all of the “sales call type” communicationsreceived by a contact center during a predetermined time frame. In thatcase, the user would analyze each of the sales call type communicationsfrom that time period by applying the predetermined psychologicalbehavioral model to each such communication.

Alternatively, telephonic communications may be grouped according tocustomer categories, and the user may desire to analyze thecommunications between the contact center and communicants within aparticular customer category. For example, it may be desirable for auser to perform an analysis only of a “platinum customers” category,consisting of high end investors, or a “high volume distributors”category comprised of a user's best distributors.

According to another embodiment, electronic customer communication dataand behavioral assessment data can be employed to assist with voicecommunications. By way of example, behavioral assessment data generatedfor a customer may be output by a contact center to an agent to betterassist the customer during a voice communication with the contactcenter. FIGS. 11-14 discuss one or more embodiments of analyzing voicecommunication data and generating behavioral assessment data. It shouldbe appreciated that the embodiments described above may be applied toand/or aggregated with voice and behavioral data for voicecommunication.

FIGS. 11A-11C are schematic diagrams of a telephonic communicationsystem according to one or more embodiments. The telephoniccommunication system of FIGS. 11A-11B include a distributed privatebranch exchange (PBX), having a public switched telephone network (PSTN)1105 connected to the PBX through a PBX switch 1110. With respect toFIGS. 11A-11B, telephonic communications may be a telephone call inwhich a telephonic signal is transmitted. The telephonic systemincluding various types of communication devices connected to thenetwork, including the telephony server 1107, a recording server 1109,telephone stations 1111, and client personal computers 1113 equippedwith telephone stations 1115.

As shown in FIG. 11A, a recording server 1109 is coupled to PBX switch1110. In FIG. 11B, recording server 1109 is coupled between a publicswitched telephone network (PSTN) 1105 and a PBX switch 1110. As may beseen in FIGS. 11A-11B, a customer sending a telephonic signal may accessa contact center through the public switched telephone network (PSTN)1105 and an automatic call distribution system (PBX/ACD) 1110 directsthe communication to one of a plurality of agent work stations, eachagent work station including, for example, a computer 1113 and atelephone 1115. According to one embodiment, when analyzing voice data,it is preferable to work from a true and clear hi-fidelity signal. Thisis true both in instances in which the voice data is being translatedinto a text format for analysis using a linguistic-based psychologicalbehavioral model thereto, or in instances in which a linguistic-basedpsychological behavioral model is being applied directly to an audiowaveform, audio stream or file containing voice data.

The PBX switch 1110 provides an interface between the PSTN 1105 and alocal network. Preferably, the interface is controlled by softwarestored on a telephony server 1107 coupled to the PBX switch 1110. ThePBX switch 1110, using interface software, connects trunk and linestation interfaces of the public switch telephone network 1105 tostations of a local network or other peripheral devices. Further, inanother embodiment, the PBX switch may be integrated within telephonyserver 1107. The stations may include various types of communicationdevices connected to the network, including the telephony server 1107, arecording server 1109, telephone stations 1111, and client personalcomputers 1113 equipped with telephone stations 1115. The local networkmay further include fax machines and modems.

Generally, in terms of hardware architecture, the telephony server 1107includes a processor, memory, and one or more input and/or output (I/O)devices (or peripherals) that are communicatively coupled via a localinterface. The processor can be any custom-made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the telephony server1107, a semiconductor based microprocessor (in the form of a microchipor chip set), a macroprocessor, or generally any device for executingsoftware instructions. The memory of the telephony server 1107 caninclude any one or a combination of volatile memory elements (e.g.,random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) andnonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.).The telephony server 1107 may further include a keyboard and a mouse forcontrol purposes, and an attached graphic monitor for observation ofsoftware operation.

The telephony server 1107 incorporates PBX control software to controlthe initiation and termination of connections between stations and viaoutside trunk connections to the PSTN 1105. In addition, the softwaremay monitor the status of all telephone stations 1111 in real-time onthe network and may be capable of responding to telephony events toprovide traditional telephone service. This may include the control andgeneration of the conventional signaling tones such as dial tones, busytones, ring back tones, as well as the connection and termination ofmedia streams between telephones on the local network. Further, the PBXcontrol software may use a multi-port module and PCs to implementstandard PBX functions such as the initiation and termination oftelephone calls, either across the network or to outside trunk lines,the ability to put calls on hold, to transfer, park and pick up calls,to conference multiple callers, and to provide caller ID information.Telephony applications such as voice mail and auto attendant may beimplemented by application software using the PBX as a network telephonyservices provider.

FIG. 11C is a schematic diagram of a telephonic communication systemwith a multi-port PSTN module according to one or more embodiments. Inone embodiment, the telephony server 1107 is equipped with multi-portPSTN module 1123 having circuitry and software to implement a trunkinterface 1117 and a local network interface 1119. The PSTN module 1123comprises a control processor 1121 to manage the transmission andreception of network messages between the PBX switch 1110 and thetelephony network server 1107. The control processor 1121 is alsocapable of directing network messages between the PBX switch 1110, thelocal network interface 291, the telephony network server 1107, and thetrunk interface 1117. In the one embodiment, the local network usesTransmission Control Protocol/Internet Protocol (TCP/IP). The networkmessages may contain computer data, telephony transmission supervision,signaling and various media streams, such as audio data and video data.The control processor 1121 directs network messages containing computerdata from the PBX switch 1110 to the telephony network server 1107directly through the multi-port PSTN module 1123.

The control processor 1121 may include buffer storage and control logicto convert media streams from one format to another, if necessary,between the trunk interface 1117 and local network. The trunk interface1117 provides interconnection with the trunk circuits of the PSTN 1105.The local network interface 1119 provides conventional software andcircuitry to enable the telephony server 1107 to access the localnetwork. The buffer RAM and control logic implement efficient transferof media streams between the trunk interface 1117, the telephony server1107, the digital signal processor 1125, and the local network interface1119.

The trunk interface 1117 utilizes conventional telephony trunktransmission supervision and signaling protocols required to interfacewith the outside trunk circuits from the PSTN 1105. The trunk linescarry various types of telephony signals such as transmissionsupervision and signaling, audio, fax, or modem data to provide plainold telephone service (POTS). In addition, the trunk lines may carryother communication formats such T1, ISDN or fiber service to providetelephony or multimedia data images, video, text or audio.

The control processor 1121 manages real-time telephony event handlingpertaining to the telephone trunk line interfaces, including managingthe efficient use of digital signal processor resources for thedetection of caller ID, DTMF, call progress and other conventional formsof signaling found on trunk lines. The control processor 1121 alsomanages the generation of telephony tones for dialing and otherpurposes, and controls the connection state, impedance matching, andecho cancellation of individual trunk line interfaces on the multi-portPSTN module 1123.

Preferably, conventional PBX signaling is utilized between trunk andstation, or station and station, such that data is translated intonetwork messages that convey information relating to real-time telephonyevents on the network, or instructions to the network adapters of thestations to generate the appropriate signals and behavior to supportnormal voice communication, or instructions to connect voice mediastreams using standard connections and signaling protocols. Networkmessages are sent from the control processor 1121 to the telephonyserver 1107 to notify the PBX software in the telephony server 1107 ofreal-time telephony events on the attached trunk lines. Network messagesare received from the PBX Switch 1110 to implement telephone callsupervision and may control the set-up and elimination of media streamsfor voice transmission.

The local network interface 1119 includes conventional circuitry tointerface with the local network. The specific circuitry is dependent onthe signal protocol utilized in the local network. In one embodiment,the local network may be a local area network (LAN) utilizing IPtelephony. IP telephony integrates audio and video stream control withlegacy telephony functions and may be supported through the H.323protocol. H.323 is an International TelecommunicationUnion—Telecommunications protocol used to provide voice and videoservices over data networks. H.323 permits users to make point-to-pointaudio and video phone calls over a local area network. IP telephonysystems can be integrated with the public telephone system through alocal network interface 1119, such as an IP/PBX-PSTN gateway, therebyallowing a user to place telephone calls from an enabled computer. Forexample, a call from an IP telephony client to a conventional telephonewould be routed on the LAN to the IP/PBX-PSTN gateway. The IP/PBX-PSTNgateway translates H.323 protocol to conventional telephone protocol androutes the call over the conventional telephone network to itsdestination. Conversely, an incoming call from the PSTN 1105 is routedto the IP/PBX-PSTN gateway and translates the conventional telephoneprotocol to H.323 protocol.

As noted above, PBX trunk control messages are transmitted from thetelephony server 1107 to the control processor 1121 of the multi-portPSTN. In contrast, network messages containing media streams of digitalrepresentations of real-time voice are transmitted between the trunkinterface 1117 and local network interface 1119 using the digital signalprocessor 1125. The digital signal processor 1125 may include bufferstorage and control logic. Preferably, the buffer storage and controllogic implement a first-in-first-out (FIFO) data buffering scheme fortransmitting digital representations of voice audio between the localnetwork to the trunk interface 1117. It is noted that the digital signalprocessor 1125 may be integrated with the control processor 1121 on asingle microprocessor.

The digital signal processor 1125 may include a coder/decoder (CODEC)connected to the control processor 1121. The CODEC may be a typeTCM29c13 integrated circuit made by Texas Instruments, Inc. In oneembodiment, the digital signal processor 1125 receives an analog ordigital voice signal from a station within the network or from the trunklines of the PSTN 1105. The CODEC converts the analog voice signal intoa digital from, such as digital data packets. It should be noted thatthe CODEC is not used when connection is made to digital lines anddevices. From the CODEC, the digital data is transmitted to the digitalsignal processor 1125 where telephone functions take place. The digitaldata is then passed to the control processor 1121 which accumulates thedata bytes from the digital signal processor 1125. It is preferred thatthe data bytes are stored in a first-in-first-out (FIFO) memory bufferuntil there is sufficient data for one data packet to be sent accordingto the particular network protocol of the local network. The specificnumber of bytes transmitted per data packet depends on network latencyrequirements as selected by one of ordinary skill in the art. Once adata packet is created, the data packet is sent to the appropriatedestination on the local network through the local network interface1119. Among other information, the data packet contains a sourceaddress, a destination address, and audio data. The source addressidentifies the location the audio data originated from and thedestination address identifies the location the audio data is to besent.

The system permits bi-directional communication by implementing a returnpath allowing data from the local network, through the local networkinterface 1119, to be sent to the PSTN 1105 through the multi-line PSTNtrunk interface 1117. Data streams from the local network are receivedby the local network interface 1119 and translated from the protocolutilized on the local network to the protocol utilized on the PSTN 1105.The conversion of data may be performed as the inverse operation of theconversion described above relating to the IP/PBX-PSTN gateway. The datastream is restored in appropriate form suitable for transmission throughto either a connected telephone 1111, 1115 or an interface trunk 1117 ofthe PSTN module 1123, or a digital interface such as a T1 line or ISDN.In addition, digital data may be converted to analog data fortransmission over the PSTN 1105.

The PBX switch 1110 may be implemented with hardware or virtually. Ahardware PBX has equipment located local to the user of the PBX system.A virtual PBX has equipment located at a central telephone serviceprovider and delivers the PBX as a service over the PSTN 1105.

The system includes a recording server 1109 for recording and separatingnetwork messages transmitted within the system. The recording server1109 may be connected to a port on the local network. Alternatively, therecording server 1109 may be connected to the PSTN trunk line asillustrated in FIG. 11A. The recording server 1109 includes controlsystem software, such as recording software. The recording software canbe implemented in software (e.g., firmware), hardware, or a combinationthereof. In one embodiment, the recording software is implemented insoftware, as an executable program, and is executed by one or morespecial or general purpose digital computer(s), such as a personalcomputer, personal digital assistant, workstation, minicomputer, ormainframe computer. An example of a general purpose computer that canimplement the recording software is shown in FIG. 2. The recordingsoftware may reside in, or have portions residing in, any computer suchas, but not limited to, a general purpose personal computer. Therefore,recording server 1109 of FIGS. 11A-11C may be representative of any typeof computer in which the recording software resides or partiallyresides.

Generally, hardware architecture is the same as that discussed above andshown in FIG. 2. Specifically, the recording server 1109 includes aprocessor, memory, and one or more input and/or output (I/O) devices (orperipherals) that are communicatively coupled via a local interface aspreviously described. The local interface can be, for example, but notlimited to, one or more buses or other wired or wireless connections, asis known in the art. The local interface may have additional elements,which are omitted for simplicity, such as controllers, buffers (caches),drivers, repeaters, and receivers, to enable communications. Further,the local interface may include address, control, and/or dataconnections to enable appropriate communications among the othercomputer components.

As noted above, the recording server 1109 incorporates recordingsoftware for recording and separating a signal based on the sourceaddress and/or destination address of the signal. The method utilized bythe recording server 1109 depends on the communication protocol utilizedon the communication lines to which the recording server 1109 iscoupled. In the communication system, the signal carrying audio data ofa communication between at least two users may be an analog signal or adigital signal in the form of a network message. In one embodiment, thesignal is an audio data transmitted according to a signaling protocol,for example the H.323 protocol described above.

An example of a communication between an outside caller and a contactcenter agent utilizing the present system is illustrated in FIG. 11C anddescribed herein. In the embodiment of FIG. 11C, when an outside callerreaches the system through the multi-line interface trunk 1117, theirvoice signal is digitized (if needed) in the manner described above, andconverted into digital data packets according to the communicationprotocol utilized on the local network of the system. The data packetcomprises a source address identifying the address of the outsidecaller, a destination address identifying the address of the contactcenter agent, and first constituent audio data comprising at least aportion of the outside caller's voice. The data packet can furthercomprise routing data identifying how the data packet should be routedthrough the system and other relevant data. Once the data packet iscreated, the data packet is sent to the appropriate destination on thelocal network, such as to a contact center agent, through the localnetwork interface 1119. The PBX and/or an automatic call distributor(ACD) can determine the initial communication setup, such as theconnection state, impedance matching, and echo cancellation, accordingto predetermined criteria.

Similar to the process described above, when the contact center agentspeaks, their voice is digitized (if needed) and converted into digitaldata packet according to the communication protocol utilized on thelocal network. The data packet comprises a source address identifyingthe address of the contact center agent, a destination addressidentifying the address of the outside caller, and second constituentaudio data comprising at least a portion of the contact center agent'svoice. The data packet is received by the local network interface 1119and translated from the communication protocol utilized on the localnetwork to the communication protocol utilized on the PSTN 1105. Theconversion of data can be performed as described above. The data packetis restored in appropriate form suitable for transmission through toeither a connected telephone 1111, 1115 or an interface trunk 1117 ofthe PSTN module 1123, or a digital interface such as a T1 line or ISDN.In addition, digital data can be converted to analog data fortransmission through the PSTN 1105.

The recording server 1109 receives either a data packet including: thesource address identifying the address of the outside caller, adestination address identifying the address of the contact center agent,and the first constituent audio data comprising at least a portion ofthe outside caller's voice; or a data packet including a source addressidentifying the address of the contact center agent, a destinationaddress identifying the address of the outside caller, and secondconstituent audio data comprising at least a portion of the customer'sagent voice. It is understood by one of ordinary skill in the art thatthe recording server 1109 is programmed to identify the communicationprotocol utilized by the local network and extract the audio data withinthe data packet. In one embodiment, the recording server 1109 canautomatically identify the utilized communication protocol from aplurality of communication protocols. The plurality of communicationprotocols can be stored in local memory or accessed from a remotedatabase.

The recording server 1109 comprises recording software to record thecommunication session between the outside caller and the contact centeragent in a single data file in a stereo format. The first data file hasat least a first audio track and a second audio track. Once a telephoneconnection is established between an outside caller and a contact centeragent, the recording software creates a first data file to record thecommunication between the outside caller and the contact center agent.The entire communication session or a portion of the communicationsession can be recorded.

In one embodiment, the recording server 1109, upon receiving the datapacket, determines whether to record the audio data contained in thedata packet in either the first audio track or the second audio track ofthe first data file as determined by the source address, destinationaddress, and/or the audio data contained within the received datapacket. Alternatively, two first data files can be created, wherein thefirst audio track is recorded to the one of the first data file and thesecond audio track is recorded to the second first data file. In oneembodiment, if the data packet comprises a source address identifyingthe address of the outside caller, a destination address identifying theaddress of the contact center agent, and first constituent audio data,the first constituent audio data is recorded on the first audio track ofthe first data file. Similarly, if the data packet comprises a sourceaddress identifying the address of the contact center agent, adestination address identifying the address of the outside caller, andsecond constituent audio data, the second constituent audio data isrecorded on the second audio track of the first data file. It should benoted the first and second constituent audio data can be a digital oranalog audio waveform or a textual translation of the digital or analogwaveform. The recording process is repeated until the communication linkbetween the outside caller and contact center agent is terminated.

As noted above, the recording server 1109 can be connected to the trunklines of the PSTN 1105 as seen in FIG. 8. The PSTN 1105 can utilize adifferent protocol and therefore, the recording server 1109 isconfigured to identify the communication protocol utilized by the PSTN1105, recognize the source and destination address of a signal andextract the audio data from the PSTN 1105. The recording server 1109 isprogrammed in a manner as known to one of ordinary skill in the art.

Once the communication link is terminated, the recording server 1109ends the recording session and stores the single data file having therecorded communication session in memory. After the first data file isstored in memory, the recording server 1109 can extract either or bothof the first constituent audio data from the first audio track of thefirst data file or the second constituent audio data from the secondaudio track of the first data file. Extraction of constituent data isdiscussed below with reference to FIGS. 12-14.

It is known in the art that “cradle-to-grave” recording may be used torecord all information related to a particular telephone call from thetime the call enters the contact center to the later of: the callerhanging up or the agent completing the transaction. All of theinteractions during the call are recorded, including interaction with anIVR system, time spent on hold, data keyed through the caller's key pad,conversations with the agent, and screens displayed by the agent athis/her station during the transaction.

FIGS. 12-14 are directed to extraction and analysis of constituent datafor voice communication. According to one embodiment, the extraction andanalysis of voice data as described in FIGS. 12-14 can be supplementedby generation of behavioral data for electronic customer communicationdata discussed above. FIG. 12 is a flow chart illustrating a process ofrecording and separating a telephonic communication according to one ormore embodiments. At block 1205, a telephonic communication can bereceived. A communication protocol associated with the telephoniccommunication can be identified at block 1210. At block 1205, thecommunication can be separated into first and second constituent voicedata.

In one embodiment shown in FIGS. 12-13, the first constituent audio dataextracted from the first audio track is stored in a first constituentdata file 1220. Similarly, the second constituent audio data extractedfrom the second audio track can be stored in a second constituent datafile 1225. The first and second constituent data files 1220 and 1225 canbe compressed before being stored in memory. The extracted data can bein the form of a digital or analog audio waveform or can be a textualtranslation of the first or second constituent audio data. Either orboth of the first constituent data file 1220 or the second constituentdata file 1225 can be further analyzed or processed. For example, amongother processes and analyses, filtering techniques can be applied to thefirst constituent data file and/or the second constituent data file.Moreover, event data, such as silence periods or over-talking, can beidentified through analysis techniques known to those skilled in theart.

Further, the first constituent data file 1220 and second constituentdata file 1225 can be merged together into a single second data file.The first and second constituent data files can be merged in a stereoformat where the first constituent audio data from the first constituentdata file 1220 is stored on a first audio track of the second data fileand the second constituent audio data from the second constituent datafile 1225 is stored on a second audio track of the second data file.Alternatively, the first and second constituent data files can be mergedin a mono format where the first constituent audio data from the firstconstituent data file 1220 and the second constituent audio data fromthe second constituent data file 1225 are stored on a first audio trackof the second data file. Additionally, the first and second constituentaudio data can be merged into a document having a textual translation ofthe audio data. In such a case, identifiers can be associated with eachof the merged first and second constituent audio data in order toassociate the merged first constituent audio data with the outsidecaller, and associate the merged second constituent audio data with thecontact center agent. The second data file can be compressed beforebeing stored in memory.

As shown in FIGS. 13-14, once the first and second constituent voicedata are separated one from the other, each of the first and secondconstituent voice data can be independently mined and analyzed.According to one embodiment, mining voice data can be considered part ofthe process of analyzing the constituent voice data. Mining andbehavioral analysis can be conducted on either or both of theconstituent voice data.

Even with conventional audio mining technology, application oflinguistic-based disparities in dialect, phonemes, accents andinflections can impede or render burdensome accurate identification ofwords. And while mining and analysis in accordance with one or moreembodiments can be applied directly to voice data configured in audioformat, in a preferred embodiment, the voice data to be mined andanalyzed is first translated into a text file.

According to another embodiment, the separated voice data is mined forbehavioral signifiers associated with a linguistic-based psychologicalbehavioral model. In particular, the method searches for and identifiestext-based keywords (i.e., behavioral signifiers) relevant to apredetermined psychological behavioral model.

According to one embodiment, PCM is the psychological behavioral modelused to analyze the voice data. According to one embodiment shown inFIG. 14, the system mines significant words within one or both of theseparated first and second constituent voice data, and applies PCM tothe identified words. In another embodiment, the present method minesfor such significant words within the merged second data file describedabove, and apply PCM to the identified words. Alternatively, the firstdata file can be mined for significant words.

FIG. 13 is a flow chart illustrating a process of recording andseparating a telephonic communication according to one or moreembodiments. It may be desirable to analyze non-linguistic phone eventsoccurring during the course of a conversation such as hold times,transfers, “dead-air,” overtalk, etc. Accordingly, in one embodiment,phone event data resulting from analysis of these non-linguistic eventsis generated. Preferably, the phone event data is generated by analyzingnon-linguistic information from both the separated constituent voicedata, or from the subsequently generated audio file containing at leastsome of the remerged audio data of the original audio waveform. Inaddition, the phone event data can be generated before the audiowaveform is separated.

Generally, call assessment data is comprised of behavioral assessmentdata, phone event data and distress assessment data. The resultant callassessment data may be subsequently viewed to provide an objectiveassessment or rating of the quality, satisfaction or appropriateness ofthe interaction between an agent and a customer. In the instance inwhich the first and second constituent voice data are comparativelyanalyzed, the call assessment data may generate resultant data usefulfor characterizing the success of the interaction between a customer andan agent.

In one embodiment, a non-linguistic based analytic tool can beseparately applied to each of the separated first and second constituentvoice data 1220 and 1225, and to generate phone event data correspondingto the analyzed voice data at blocks 1305 and 1306. The separated firstand second constituent voice data 1220 and 1225 is translated into textformat and stores the respective translated first and second constituentvoice data in a first and second text file at blocks 1310 and 1311. Thefirst and second text files are analyzed by applying a predeterminedlinguistic-based psychological behavioral model thereto at blocks 1315and 1316. The code segment generates either or both of behavioralassessment data and distress assessment data corresponding to each ofthe analyzed first and second constituent voice data at blocks 1315 and1316. The resulting behavioral assessment data from each of the analyzedfirst and second constituent voice data are comparatively analyzed atblock 1320 in view of the parameters of the psychological behavioralmodel to provide an assessment of a given communication. From thiscomparative analysis, call assessment data relating to the totality ofthe call may be generated at block 1325.

FIG. 14 is a flow chart illustrating a process of analyzing separatedconstituent voice data of a telephonic communication in according to oneor more embodiments. According to a one, both the first and secondconstituent voice data are analyzed at blocks 1405 and 1406, translatedfrom voice to text data at blocks 1410 and 1411, and mined at blocks1425 and 1426. Mined behavioral data is checked to see if behavioralsignifiers are associated with types at blocks 1420 and 1421 and matchedto types at blocks 1425 and 1426, in particular types 1430. Theresulting behavioral assessment data generated at blocks 1435 and 1436,phone event data at block 1450 and distress assessment data 1450 fromeach of the analyzed first and second constituent voice data arecomparatively analyzed in view of the parameters of the psychologicalbehavioral model to provide an assessment of a given communication. Fromthis comparative analysis, call assessment data relating to the totalityof the call may be generated at block 1440 and output at block 1445.

As shown in FIG. 14, when a behavioral signifier is identified withinthe voice data at blocks 1415 and 1416, the identified behavioralsignifier is executed against a system database that maintains all ofthe data related to the psychological behavioral model. Based on thebehavioral signifiers identified in the analyzed voice data, apredetermined algorithm at blocks 1420 and 1421 is used to decipher alinguistic pattern that corresponds to one or more of the PCMpersonality types 1430. More specifically, the method of FIG. 14 minesfor linguistic indicators (words and phrases) that reveal the underlyingpersonality characteristics of the speaker during periods of distress.Non-linguistic indicators may also be identified to augment or confirmthe selection of a style for each segment of speech. Looking at all thespeech segments in conjunction with personality information the softwaredetermines an order of personality components for the caller by weighinga number of factors such as timing, position, quantity and interactionbetween the parties in the dialog.

The resultant behavioral assessment data at blocks 1435 and 1436 isstored in a database so that it may subsequently be used tocomparatively analyze against behavioral assessment data derived fromanalysis of the other of the first and second constituent voice data atblock 1440. The software considers the speech segment patterns of allparties in the dialog as a whole to refine the behavioral and distressassessment data of each party, making sure that the final distress andbehavioral results are consistent with patterns that occur in humaninteraction. Alternatively, the raw behavioral assessment data at blocks1435 and 1436 derived from the analysis of the single voice data may beused to evaluate qualities of a single communicant (e.g., the customeror agent behavioral type, etc.). The results generated by analyzingvoice data through application of a psychological behavioral model toone or both of the first and second constituent voice data can begraphically illustrated as discussed in further detail below.

It should be noted that, although one preferred embodiment uses PCM as alinguistic-based psychological behavioral model, any knownlinguistic-based psychological behavioral model be employed, or morethan one linguistic-based psychological behavioral model be used toanalyze one or both of the first and second constituent voice data.

In addition to the behavioral assessment of voice data, the method mayalso employ distress analysis to voice data. As may be seen in FIG. 14,linguistic-based distress analysis is preferably conducted on both thetextual translation of the voice data and the audio file containingvoice data. Accordingly, linguistic-based analytic tools as well asnon-linguistic analytic tools may be applied to the audio file. Forexample, one of skill in the art may apply spectral analysis to theaudio file voice data while applying a word spotting analytical tool tothe text file. Linguistic-based word spotting analysis and algorithmsfor identifying distress can be applied to the textual translation ofthe communication. Preferably, the resultant distress data is stored ina database for subsequent analysis of the communication.

The method and system is useful in improving the quality of customerinteractions with agents and ultimately customer relationships. In use,a customer wishing to engage in a service call, a retention call or asales will call into (or be called by) a contact center. When the callenters the contact center it will be routed by appropriate means to acontact center agent. As the interaction transpires, the voice data willbe recorded as described herein. Either contemporaneously with theinteraction, or after the call interaction has concluded, the recordedvoice data will be analyzed as described herein. The results of theanalysis will generate call assessment data comprised of behavioralassessment data, distress assessment data and phone event data. Thisdata may be subsequently used by a supervisor or trainer to evaluate anagent, or take other remedial action such as call back the customer,etc. Also, graphical and pictorial analysis of the resultant callassessment data (and event data) will be accessible through a portal bya subsequent user (e.g., a supervisor, training instructor or monitor)through a graphical user interface.

A user of the system described above interacts with the system via aunique GUI. The GUI enables the user to navigate through the system toobtain desired reports and information regarding the caller interactionevents stored in memory. The GUI can be part of a software programresiding in whole or in part in a computing device, or it may reside inwhole or in part on a server coupled to a computing device via a networkconnection, such as through the Internet or a local or wide area network(LAN or WAN). Moreover, a wireless connection can be used to link to thenetwork.

The foregoing outlines features of several embodiments so that a personof ordinary skill in the art may better understand the aspects of thepresent disclosure. Such features may be replaced by any one of numerousequivalent alternatives, only some of which are disclosed herein. One ofordinary skill in the art should appreciate that they may readily usethe present disclosure as a basis for designing or modifying otherprocesses and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein. Oneof ordinary skill in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions andalterations herein without departing from the spirit and scope of thepresent disclosure.

The Abstract at the end of this disclosure is provided to comply with 37C.F.R. §1.72(b) to allow the reader to quickly ascertain the nature ofthe technical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

What is claimed is:
 1. A method for analyzing electronic customercommunication data and generating behavioral assessment data, the methodcomprising the acts of: receiving electronic customer communication databy a contact center; determining customer identification data associatedwith the electronic customer communication data by the contact center;analyzing the electronic customer communication data by applying apredetermined linguistic-based psychological behavioral model to theelectronic customer communication data; and generating behavioralassessment data by the contact center based on said analyzing, thebehavioral assessment data providing a personality type for the analyzedelectronic customer communication data.
 2. The method of claim 1,wherein the electronic customer communication data is at least one ofelectronic-mail data, web content data, text message data, voice over IPdata, and online forum data.
 3. The method of claim 1, wherein theelectronic customer communication data is social media data, updatestatus, media feed, social media review, and a social media data stream.4. The method of claim 1, wherein the electronic customer communicationdata is data provided by a customer during communication with thecontact center and is associated with one or more of a pop-up windowmessage, computer display, application window, and graphical userinterface for the contact center.
 5. The method of claim 1, wherein theanalyzing includes identifying the type of electronic customercommunication data, and wherein the analyzing is based on the type ofelectronic communication data.
 6. The method of claim 1, whereinanalyzing includes determining identifying indicia of a customer and abehavioral signifier in the electronic customer data.
 7. The method ofclaim 1, wherein analyzing includes determining a customer activityassociated with the contact center based on the electronic customercommunication data.
 8. The method of claim 1, wherein further comprisingoutputting a notification including the behavioral assessment data bythe contact center based on detection of the customer identificationdata.
 9. The method of claim 1, further comprising storing thebehavioral assessment data, wherein the behavioral assessment dataincludes identifying indicia associated with a customer.
 10. The methodof claim 1, further comprising automatically generating responsiveelectronic communication to the electronic customer communication data.11. The method of claim 1, further comprising analyzing a telephoniccommunication associated with the electronic customer communicationdata, wherein analyzing the telephonic voice communication includes:receiving a telephonic communication between a first communicant to thetelephonic communication and a second communicant to the telephoniccommunication; separating the telephonic communication into at leastfirst constituent voice data, the first constituent voice data beinggenerated by the first communicant and the second constituent voice databeing generated by the second communicant; analyzing one of theseparated first and second constituent voice data by mining theseparated one of the first and second constituent voice data andapplying a predetermined linguistic-based psychological behavioral modelto the one of the separated first and second constituent voice data;and, generating behavioral assessment data including a personality typecorresponding to the analyzed one of the separated first and secondconstituent voice data based on the analyzing of constituent voice dataand based on behavioral assessment data for the electronic customercommunication data.
 12. The method of claim 11, wherein at least one ofthe first and second constituent voice data is aggregated with theelectronic communication data, and a text file is generated fromaggregated data for applying the predetermined linguistic-basedpsychological behavioral model.
 13. The method of claim 1, furthercomprising: aggregating electronic customer communication data from oneor more channels; and generating a text file from aggregated electroniccustomer communication data for the behavioral assessment data.
 14. Themethod of claim 1, further comprising aggregating electronic customercommunication data from one or more sources based on identification of acustomer from the electronic customer communication data.
 15. A computerprogram product stored on a non-transitory computer readable mediumincluding computer executable code for analyzing electronic customercommunication data and generating behavioral assessment data, thecomputer program product comprising: computer readable code to receiveelectronic customer communication data by a contact center; computerreadable code to determine customer identification data associated withthe electronic customer communication data by the contact center;computer readable code to analyze the electronic customer communicationdata by applying a predetermined linguistic-based psychologicalbehavioral model to the electronic customer communication data; andcomputer readable code to generate behavioral assessment data by thecontact center based on said analyzing, the behavioral assessment dataproviding a personality type for the analyzed electronic customercommunication data.
 16. The computer program product of claim 15,wherein the electronic customer communication data is at least one ofelectronic-mail data, web content data, text message data, voice over IPdata, and online forum data.
 17. The computer program product of claim15, wherein the electronic customer communication data is social mediadata, update status, media feed, social media review, and a social mediadata stream.
 18. The computer program product of claim 15, wherein theelectronic customer communication data is data provided by a customerduring communication with the contact center and is associated with oneor more of a pop-up window message, computer display, applicationwindow, and graphical user interface for the contact center.
 19. Thecomputer program product of claim 15, wherein analyzing includesidentifying the type of electronic communication data, and wherein theanalyzing is based on the type of electronic customer communicationdata.
 20. The computer program product of claim 15, wherein analyzingincludes determining identifying indicia of a customer and a behavioralsignifier in the electronic customer data.
 21. The computer programproduct of claim 15, wherein analyzing includes determining a customeractivity associated with the contact center based on the electroniccustomer communication data.
 22. The computer program product of claim15, further comprising computer readable code to output a notificationincluding the behavioral assessment data by the contact center based ondetection of the customer identification data.
 23. The computer programproduct of claim 15, further comprising computer readable code to storethe behavioral assessment data, wherein the behavioral assessment dataincludes identifying indicia associated with a customer.
 24. Thecomputer program product of claim 15, further comprising computerreadable code to automatically generate responsive electroniccommunication to the electronic customer communication data.
 25. Thecomputer program product of claim 15, further comprising computerreadable code to analyze a telephonic communication associated with theelectronic customer communication, wherein analyzing the telephonicvoice communication includes: receiving a telephonic communicationbetween a first communicant to the telephonic communication and a secondcommunicant to the telephonic communication; separating the telephoniccommunication into at least first constituent voice data, the firstconstituent voice data being generated by the first communicant and thesecond constituent voice data being generated by the second communicant;analyzing one of the separated first and second constituent voice databy mining the separated one of the first and second constituent voicedata and applying a predetermined linguistic-based psychologicalbehavioral model to the one of the separated first and secondconstituent voice data; and, generating behavioral assessment dataincluding a personality type corresponding to the analyzed one of theseparated first and second constituent voice data based on the analyzingof constituent voice data and based on behavioral assessment data forthe electronic customer communication data.
 26. The computer programproduct of claim 25, wherein at least one of the first and secondconstituent voice data is aggregated with the electronic communicationdata, and a text file is generated from aggregated data for applying thepredetermined linguistic-based psychological behavioral model.
 27. Thecomputer program product of claim 15, further comprising: computerreadable code to aggregate electronic customer communication data fromone or more channels; and computer readable code to generate a text filefrom aggregated electronic customer communication data for thebehavioral assessment data.
 28. The computer program product of claim 1,further comprising computer readable code to aggregate electroniccustomer communication data from one or more sources based onidentification of a customer from the electronic customer communicationdata.
 29. A method for analyzing electronic customer communication dataand generating behavioral assessment data, the method comprising theacts of: receiving electronic customer communication data by a contactcenter; determining customer identification data associated with theelectronic customer communication data by the contact center;aggregating electronic customer communication data from one or moresources based on identification of a customer from the electroniccustomer communication data; analyzing the aggregated electroniccustomer communication data by applying a predetermined linguistic-basedpsychological behavioral model to the electronic customer communicationdata; and generating behavioral assessment data by the contact centerbased on said analyzing, the behavioral assessment data providing apersonality type for analyzed electronic customer communication data.30. The method of claim 29, wherein the electronic customercommunication data is at least one of electronic-mail data, web contentdata, text message data, voice over IP data, and online forum data. 31.The method of claim 29, wherein the electronic customer communicationdata is social media data, update status, media feed, social mediareview, and a social media data stream.
 32. The method of claim 29,wherein the electronic customer communication data is data provided by acustomer during communication with the contact center and is associatedwith one or more of a pop-up window message, computer display,application window, and graphical user interface for the contact center.33. The method of claim 29, wherein the analyzing includes identifyingthe type of electronic customer communication data, and wherein theanalyzing is based on the type of electronic communication data.
 34. Themethod of claim 29, wherein analyzing includes determining a behavioralsignifier in the electronic customer data.
 35. The method of claim 29,wherein analyzing includes determining a customer activity typeassociated with the contact center based on the electronic customercommunication data.
 36. The method of claim 29, further comprisingstoring the behavioral assessment data, wherein the behavioralassessment data includes customer identification data.
 37. The method ofclaim 29, further comprising analyzing a telephonic communicationassociated with the electronic customer communication data, whereinanalyzing the telephonic voice communication includes: receiving atelephonic communication between a first communicant to the telephoniccommunication and a second communicant to the telephonic communication;separating the telephonic communication into at least first constituentvoice data, the first constituent voice data being generated by thefirst communicant and the second constituent voice data being generatedby the second communicant; analyzing one of the separated first andsecond constituent voice data by mining the separated one of the firstand second constituent voice data and applying a predeterminedlinguistic-based psychological behavioral model to the one of theseparated first and second constituent voice data; and, generatingbehavioral assessment data including a personality type corresponding tothe analyzed one of the separated first and second constituent voicedata based on the analyzing of constituent voice data and based onbehavioral assessment data for the electronic customer communicationdata.
 38. The method of claim 37, wherein at least one of the first andsecond constituent voice data is aggregated with the electroniccommunication data, and a text file is generated from aggregated datafor applying the predetermined linguistic-based psychological behavioralmodel.
 39. The method of claim 29, further comprising: aggregatingelectronic customer communication data from one or more channels basedon the customer identification data; and generating a text file fromaggregated electronic customer communication data for the behavioralassessment data.
 40. The method of claim 29, further comprisingoutputting a notification including the behavioral assessment data bythe contact center based on detection of the customer identificationdata.